
Abstract
The current energy consumption of the worlds population relies heavily on fossil
fuels. Unfortunately, the consumption of fossil fuels not only results in the
emission of greenhouse gases which have deleterious effect on the envrionment
but also the fossil fuel reserve is limited. Therefore, it is the need of the hour
to search for environmentally benign renewable energy resources. The biggest
source of the renewable energy is our sun and the immense energy it provides
can be used to power the whole planet. However, an efficient way to harvest
the solar energy to meet all the energy demand has not been realized yet.
A promising way to utilize the solar energy is the photon assisted water
splitting. The process involves the absorption of sunlight with a semiconducting
material (or a photoabsorber) and the generated electron-hole pair can be
used to produce hydrogen by splitting the water. However, a single material
cannot accomplish the whole process of the hydrogen evolution. In order do
so, a material should be able to absorb the sunlight and generate the electronhole
pairs and evolve hydrogen at the cathode and oxygen at anode using the
generated electron and hole respectively.
This thesis using first-principle calculations explores materials for the light
absorption with the bandgap, band edge positions and the stability in aqueous
conditions as descriptors. This strategy results in a handful of materials which
can act as good photoabsorbers for the water splitting reaction. Additionally,
strategies to tune the bandgap for different applications is also explored. To
carry out the cathode reaction, two-dimensional metal dichalcogenides and oxides
are explored with a suggestion of few potential candidates for the hydrogen
evolution reaction.
The thermodynamics of all the above process requires an accurate description
of the energies with the first-principle calculations. Therefore, along this
line the accuracy and predictability of the Meta-Generalized Gradient Approximation
functional with Bayesian error estimation is also assessed.